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| Dr. Paul M. Torrens, Center for Urban Science + Progress, New York University | ||||||
Machine-learning behavioral geography
| Project overview | Eye candy | Demo movie | Support | Related groups |
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| Project overview |
The goal of this project is to machine-learn behavioral rules for agent-based models, using data-mining and knowledge discovery on massive databases of trajectory samples from diverse sources. These data may come from location-aware hardware, such as Geographic Positioning Systems, alternative positioning systems (Wi-Fi, cell-phone triangulation), from geocoded trip diaries, or from observation. We are developing a scheme that can work with any of these data types, using only the simplest of geographical information: location in space and time. This will allow us to build agent-based models for situations in which no theory exists, or to use machine-learning to better support theory-driven models by allying them to the real-world behavioral geography of actual people, in actual places, engaged in actual activities. The scheme works as a combination of spatial database management, spatial data access, spatial analysis, classification, clustering, and weighted training. Initially, we are using data from a three-year observational study, for which we developed a customized space-time GIS observation and data-warehousing scheme. |
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| Movie |
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| Eye candy |
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| The figure above illustrates sample trajectories from our three-year observational study, collected using a customized space-time GIS system that we developed to run on mobile hardware. |
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| The figure above illustrates the machine-learned agent-based model running in real-time, constantly learning its path through space and time using only a library of trajectory samples and our knowledge discovery model. |
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| Support |
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Torrens, P.M; Ghanem, Roger; Kevrekidis, Yannis (2010-2011). "Accelerating innovation in agent-based simulations: Application to complex socio-behavioral phenomena". National Science Foundation (Division of Civil and Mechanical Systems) |
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Torrens, P.M. (2007-2012) “CAREER: Exploring the dynamics of individual pedestrian and crowd behavior in dense urban settings: a computational approach”. National Science Foundation (Faculty Early Career Development (CAREER); Geography & Regional Science/ Methodology, Measurement, and Statistics) |
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| Related groups | |
| GAMMA group at University of North Carolina, Chapel Hill | |
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 Robot motion control |
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 Human behavior in critical scenarios |
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 Modeling riots |
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A toolkit for measuring sprawl
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 Simulating crowd behavior |
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 Wi-Fi geography |
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 Simulating sprawl |
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